Engineers racing to consume more AI tokens face soaring bills and slower responses. The article coins Tokenminning, a disciplined approach that trims token usage while preserving or improving model output. It contrasts with the wasteful tokenmaxxing mindset that assumes bigger prompts equal better results, a belief now proving costly.

The author, head of AI at a biotech startup, shares personal usage data: interactive chats generate roughly 492,165 input tokens daily, while autonomous agents consume 56,497 input and 4,594 output tokens per invocation, totaling about 1.22 million tokens a day. At current rates for Claude Opus, that translates to roughly $40 per day, a figure that can balloon to thousands for heavy‑duty agents.

Practical tokenminning tactics start with routing simple requests to cheaper, possibly local, models. A lightweight “LLM gateway” classifies prompts using a pretrained classifier—such as NVIDIA’s Nemo Curator—and directs each to the appropriate model. By avoiding unnecessary frontier‑model calls, teams can slash API spend without major code changes, delivering measurable cost savings today.